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   "source": [
    "## One-Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function that performs one-hot encoding for class labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.preprocessing import one_hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typical supervised machine learning algorithms for classifications assume that the class labels are *nominal* (a special case of *categorical* where no order is implied). A typical example of an nominal feature would be \"color\" since we can't say (in most applications) that \"orange > blue > red\".\n",
    "\n",
    "The `one_hot` function provides a simple interface to convert class label integers into a so-called one-hot array, where each unique label is represented as a column in the new array.\n",
    "\n",
    "For example, let's assume we have 5 data points from 3 different classes: 0, 1, and 2.\n",
    "\n",
    "    y = [0, # sample 1, class 0 \n",
    "         1, # sample 2, class 1 \n",
    "         0, # sample 3, class 0\n",
    "         2, # sample 4, class 2\n",
    "         2] # sample 5, class 2\n",
    " \n",
    "After one-hot encoding, we then obtain the following array (note that the index position of the \"1\" in each row denotes the class label of this sample):\n",
    "\n",
    "    y = [[1,  0,  0], # sample 1, class 0 \n",
    "         [0,  1,  0], # sample 2, class 1  \n",
    "         [1,  0,  0], # sample 3, class 0\n",
    "         [0,  0,  1], # sample 4, class 2\n",
    "         [0,  0,  1]  # sample 5, class 2\n",
    "         ]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.preprocessing import one_hot\n",
    "import numpy as np\n",
    "\n",
    "y = np.array([0, 1, 2, 1, 2])\n",
    "one_hot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Python Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.preprocessing import one_hot\n",
    "\n",
    "y = [0, 1, 2, 1, 2]\n",
    "one_hot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3 - Integer Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 1, 0],\n",
       "       [0, 0, 1]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.preprocessing import one_hot\n",
    "\n",
    "y = [0, 1, 2, 1, 2]\n",
    "one_hot(y, dtype='int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4 - Arbitrary Numbers of Class Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.preprocessing import one_hot\n",
    "\n",
    "y = [0, 1, 2, 1, 2]\n",
    "one_hot(y, num_labels=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## one_hot\n",
      "\n",
      "*one_hot(y, num_labels='auto', dtype='float')*\n",
      "\n",
      "One-hot encoding of class labels\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `y` : array-like, shape = [n_classlabels]\n",
      "\n",
      "    Python list or numpy array consisting of class labels.\n",
      "\n",
      "- `num_labels` : int or 'auto'\n",
      "\n",
      "    Number of unique labels in the class label array. Infers the number\n",
      "    of unique labels from the input array if set to 'auto'.\n",
      "\n",
      "- `dtype` : str\n",
      "\n",
      "    NumPy array type (float, float32, float64) of the output array.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `ary` : numpy.ndarray, shape = [n_classlabels]\n",
      "\n",
      "    One-hot encoded array, where each sample is represented as\n",
      "    a row vector in the returned array.\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/](http://rasbt.github.io/mlxtend/user_guide/preprocessing/one_hot/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.preprocessing/one_hot.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
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